Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7ff1ad8e2080>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7ff1ad811390>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate_input')
    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.2
    keep_prob=0.6
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28x1
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = tf.maximum(alpha * x1, x1)
        relu1 = tf.contrib.layers.dropout(relu1, keep_prob, is_training=True) 
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True) 
        relu2 = tf.maximum(alpha * bn2, bn2)
        relu2 = tf.contrib.layers.dropout(relu2, keep_prob, is_training=True) 
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        relu3 = tf.contrib.layers.dropout(relu3, keep_prob, is_training=True)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
#         flat = tf.contrib.layers.dropout(flat, keep_prob, is_training=True)
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        
        return out, logits



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.0
    reuse = not is_train
    keep_prob = 0.5
    with tf.variable_scope('generator', reuse=reuse):
        # First fully connected layer
        x1 = tf.layers.dense(z, 4*4*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        x1 = tf.contrib.layers.dropout(x1, keep_prob, is_training=is_train) 
        # 4x4x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid',kernel_initializer=tf.contrib.layers.xavier_initializer())
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        x2 = tf.contrib.layers.dropout(x2, keep_prob, is_training=is_train) 
        # 7x7x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer()) 
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        x3 = tf.contrib.layers.dropout(x3, keep_prob, is_training=is_train) 
        # 14x14x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same',kernel_initializer=tf.contrib.layers.xavier_initializer())
        # 28x28x1 now
        
        out = tf.tanh(logits)
        
        return out    


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    smooth = 0.1
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake

    return d_loss, g_loss    

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)

    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
#     saver = tf.train.Saver()
    print_every=100 
    show_every=100
    samples, losses = [], []
    steps = 0
    
    input_real, input_z, learning_rate_input = model_inputs(data_shape[-3], data_shape[-2], data_shape[-1], z_dim)    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[-1])    
    d_opt, g_opt = model_opt(d_loss, g_loss, learning_rate_input, beta1)    
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            saver = tf.train.Saver()
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
#                 batch_images = batch_images[0].reshape((batch_size, 784))
                batch_images = batch_images*2
                # Sample random noise for G
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))

                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learning_rate_input: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learning_rate_input: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, input_real: batch_images, learning_rate_input: learning_rate})
                if steps % print_every == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    # Save losses to view after training
                    losses.append((train_loss_d, train_loss_g))

                if steps % show_every == 0:
                    show_generator_output(sess, 25, input_z, data_shape[-1], data_image_mode)

#             saver.save(sess, './checkpoints/generator_{:02d}.ckpt'.format(epoch_i))

#     with open('samples.pkl', 'wb') as f:
#         pkl.dump(samples, f)
    
    return losses, samples                
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 16
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.2308... Generator Loss: 2.4433
Epoch 1/2... Discriminator Loss: 1.1385... Generator Loss: 1.0110
Epoch 1/2... Discriminator Loss: 1.6902... Generator Loss: 1.2407
Epoch 1/2... Discriminator Loss: 1.6180... Generator Loss: 1.1948
Epoch 1/2... Discriminator Loss: 1.7692... Generator Loss: 0.9619
Epoch 1/2... Discriminator Loss: 1.2149... Generator Loss: 1.1925
Epoch 1/2... Discriminator Loss: 1.4591... Generator Loss: 0.8913
Epoch 1/2... Discriminator Loss: 1.5016... Generator Loss: 0.9713
Epoch 1/2... Discriminator Loss: 1.2342... Generator Loss: 0.9678
Epoch 1/2... Discriminator Loss: 1.5565... Generator Loss: 0.6590
Epoch 1/2... Discriminator Loss: 1.1802... Generator Loss: 0.8818
Epoch 1/2... Discriminator Loss: 1.4041... Generator Loss: 0.9021
Epoch 1/2... Discriminator Loss: 1.2196... Generator Loss: 0.9490
Epoch 1/2... Discriminator Loss: 1.1571... Generator Loss: 0.6992
Epoch 1/2... Discriminator Loss: 1.3254... Generator Loss: 1.1146
Epoch 1/2... Discriminator Loss: 1.3376... Generator Loss: 0.8317
Epoch 1/2... Discriminator Loss: 1.2852... Generator Loss: 0.9649
Epoch 1/2... Discriminator Loss: 1.2096... Generator Loss: 1.0609
Epoch 1/2... Discriminator Loss: 1.3656... Generator Loss: 0.9753
Epoch 1/2... Discriminator Loss: 1.2933... Generator Loss: 0.8229
Epoch 1/2... Discriminator Loss: 1.3811... Generator Loss: 0.8384
Epoch 1/2... Discriminator Loss: 1.2562... Generator Loss: 1.0548
Epoch 1/2... Discriminator Loss: 1.4775... Generator Loss: 0.8297
Epoch 1/2... Discriminator Loss: 1.3146... Generator Loss: 1.1459
Epoch 1/2... Discriminator Loss: 1.2333... Generator Loss: 0.9475
Epoch 1/2... Discriminator Loss: 1.0620... Generator Loss: 1.0107
Epoch 1/2... Discriminator Loss: 1.4248... Generator Loss: 0.7944
Epoch 1/2... Discriminator Loss: 1.5584... Generator Loss: 0.8119
Epoch 1/2... Discriminator Loss: 1.2643... Generator Loss: 1.0678
Epoch 1/2... Discriminator Loss: 1.2795... Generator Loss: 1.0145
Epoch 1/2... Discriminator Loss: 1.4750... Generator Loss: 0.9320
Epoch 1/2... Discriminator Loss: 1.1838... Generator Loss: 0.9991
Epoch 1/2... Discriminator Loss: 1.2865... Generator Loss: 1.0860
Epoch 1/2... Discriminator Loss: 1.0779... Generator Loss: 1.0315
Epoch 1/2... Discriminator Loss: 1.1246... Generator Loss: 1.0395
Epoch 1/2... Discriminator Loss: 1.1901... Generator Loss: 0.6420
Epoch 1/2... Discriminator Loss: 1.3879... Generator Loss: 1.0212
Epoch 2/2... Discriminator Loss: 1.2876... Generator Loss: 0.7788
Epoch 2/2... Discriminator Loss: 1.3752... Generator Loss: 0.9181
Epoch 2/2... Discriminator Loss: 1.2529... Generator Loss: 0.7655
Epoch 2/2... Discriminator Loss: 1.4131... Generator Loss: 1.1920
Epoch 2/2... Discriminator Loss: 1.1804... Generator Loss: 1.2273
Epoch 2/2... Discriminator Loss: 1.1635... Generator Loss: 1.1486
Epoch 2/2... Discriminator Loss: 1.0519... Generator Loss: 1.1558
Epoch 2/2... Discriminator Loss: 1.2345... Generator Loss: 0.6954
Epoch 2/2... Discriminator Loss: 1.4502... Generator Loss: 1.0611
Epoch 2/2... Discriminator Loss: 1.2234... Generator Loss: 0.9842
Epoch 2/2... Discriminator Loss: 1.3129... Generator Loss: 1.0188
Epoch 2/2... Discriminator Loss: 1.2856... Generator Loss: 1.0126
Epoch 2/2... Discriminator Loss: 1.6383... Generator Loss: 0.8133
Epoch 2/2... Discriminator Loss: 1.3872... Generator Loss: 0.9116
Epoch 2/2... Discriminator Loss: 1.2478... Generator Loss: 1.1892
Epoch 2/2... Discriminator Loss: 1.3454... Generator Loss: 0.8469
Epoch 2/2... Discriminator Loss: 1.1029... Generator Loss: 1.3545
Epoch 2/2... Discriminator Loss: 1.2324... Generator Loss: 1.0222
Epoch 2/2... Discriminator Loss: 1.3330... Generator Loss: 0.8403
Epoch 2/2... Discriminator Loss: 1.2546... Generator Loss: 0.8830
Epoch 2/2... Discriminator Loss: 1.3502... Generator Loss: 1.0151
Epoch 2/2... Discriminator Loss: 1.3373... Generator Loss: 0.9211
Epoch 2/2... Discriminator Loss: 1.1196... Generator Loss: 1.0915
Epoch 2/2... Discriminator Loss: 1.0589... Generator Loss: 1.0689
Epoch 2/2... Discriminator Loss: 1.0418... Generator Loss: 0.9479
Epoch 2/2... Discriminator Loss: 1.1652... Generator Loss: 1.2967
Epoch 2/2... Discriminator Loss: 1.2106... Generator Loss: 0.8071
Epoch 2/2... Discriminator Loss: 1.1274... Generator Loss: 0.9812
Epoch 2/2... Discriminator Loss: 1.0972... Generator Loss: 1.0259
Epoch 2/2... Discriminator Loss: 1.1820... Generator Loss: 1.7449
Epoch 2/2... Discriminator Loss: 1.3662... Generator Loss: 0.5401
Epoch 2/2... Discriminator Loss: 1.1813... Generator Loss: 1.2838
Epoch 2/2... Discriminator Loss: 1.2560... Generator Loss: 1.1429
Epoch 2/2... Discriminator Loss: 1.1282... Generator Loss: 1.3717
Epoch 2/2... Discriminator Loss: 1.3056... Generator Loss: 1.4494
Epoch 2/2... Discriminator Loss: 1.4189... Generator Loss: 0.7991
Epoch 2/2... Discriminator Loss: 1.1961... Generator Loss: 0.7640
Epoch 2/2... Discriminator Loss: 0.9313... Generator Loss: 1.5281

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 16
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 0.8931... Generator Loss: 2.1464
Epoch 1/1... Discriminator Loss: 0.4985... Generator Loss: 3.4365
Epoch 1/1... Discriminator Loss: 0.6600... Generator Loss: 1.5466
Epoch 1/1... Discriminator Loss: 1.3002... Generator Loss: 0.9157
Epoch 1/1... Discriminator Loss: 1.1082... Generator Loss: 1.3066
Epoch 1/1... Discriminator Loss: 1.1191... Generator Loss: 0.9242
Epoch 1/1... Discriminator Loss: 1.1228... Generator Loss: 1.0588
Epoch 1/1... Discriminator Loss: 1.2400... Generator Loss: 0.9134
Epoch 1/1... Discriminator Loss: 1.3861... Generator Loss: 1.2294
Epoch 1/1... Discriminator Loss: 1.0601... Generator Loss: 0.9710
Epoch 1/1... Discriminator Loss: 1.7480... Generator Loss: 0.7756
Epoch 1/1... Discriminator Loss: 1.2352... Generator Loss: 0.9836
Epoch 1/1... Discriminator Loss: 1.2083... Generator Loss: 1.3413
Epoch 1/1... Discriminator Loss: 1.2422... Generator Loss: 0.9228
Epoch 1/1... Discriminator Loss: 1.6532... Generator Loss: 0.8981
Epoch 1/1... Discriminator Loss: 1.5194... Generator Loss: 0.5298
Epoch 1/1... Discriminator Loss: 1.5757... Generator Loss: 0.8904
Epoch 1/1... Discriminator Loss: 1.5734... Generator Loss: 0.8387
Epoch 1/1... Discriminator Loss: 1.3147... Generator Loss: 1.1065
Epoch 1/1... Discriminator Loss: 1.4255... Generator Loss: 0.8359
Epoch 1/1... Discriminator Loss: 1.3132... Generator Loss: 0.6724
Epoch 1/1... Discriminator Loss: 1.3840... Generator Loss: 0.8896
Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.7725
Epoch 1/1... Discriminator Loss: 1.2661... Generator Loss: 0.6519
Epoch 1/1... Discriminator Loss: 1.4099... Generator Loss: 0.9341
Epoch 1/1... Discriminator Loss: 1.2631... Generator Loss: 0.9695
Epoch 1/1... Discriminator Loss: 1.3352... Generator Loss: 0.8413
Epoch 1/1... Discriminator Loss: 1.4331... Generator Loss: 0.7544
Epoch 1/1... Discriminator Loss: 1.3909... Generator Loss: 0.8030
Epoch 1/1... Discriminator Loss: 1.3968... Generator Loss: 0.7928
Epoch 1/1... Discriminator Loss: 1.4283... Generator Loss: 0.8751
Epoch 1/1... Discriminator Loss: 1.3655... Generator Loss: 0.9353
Epoch 1/1... Discriminator Loss: 1.4155... Generator Loss: 0.8600
Epoch 1/1... Discriminator Loss: 1.5369... Generator Loss: 0.9190
Epoch 1/1... Discriminator Loss: 1.3564... Generator Loss: 0.8012
Epoch 1/1... Discriminator Loss: 1.4965... Generator Loss: 0.5587
Epoch 1/1... Discriminator Loss: 1.4373... Generator Loss: 0.7840
Epoch 1/1... Discriminator Loss: 1.3725... Generator Loss: 0.9789
Epoch 1/1... Discriminator Loss: 1.3203... Generator Loss: 0.6712
Epoch 1/1... Discriminator Loss: 1.3835... Generator Loss: 0.8549
Epoch 1/1... Discriminator Loss: 1.4527... Generator Loss: 0.6364
Epoch 1/1... Discriminator Loss: 1.4291... Generator Loss: 0.9757
Epoch 1/1... Discriminator Loss: 1.4119... Generator Loss: 0.8758
Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.8302
Epoch 1/1... Discriminator Loss: 1.4506... Generator Loss: 0.6607
Epoch 1/1... Discriminator Loss: 1.3400... Generator Loss: 0.8083
Epoch 1/1... Discriminator Loss: 1.3807... Generator Loss: 0.7822
Epoch 1/1... Discriminator Loss: 1.3898... Generator Loss: 0.7862
Epoch 1/1... Discriminator Loss: 1.4662... Generator Loss: 0.6721
Epoch 1/1... Discriminator Loss: 1.3856... Generator Loss: 0.7623
Epoch 1/1... Discriminator Loss: 1.3896... Generator Loss: 1.0011
Epoch 1/1... Discriminator Loss: 1.4640... Generator Loss: 0.6807
Epoch 1/1... Discriminator Loss: 1.3769... Generator Loss: 0.8839
Epoch 1/1... Discriminator Loss: 1.4446... Generator Loss: 0.8471
Epoch 1/1... Discriminator Loss: 1.4488... Generator Loss: 0.9160
Epoch 1/1... Discriminator Loss: 1.4864... Generator Loss: 0.8825
Epoch 1/1... Discriminator Loss: 1.3580... Generator Loss: 0.9540
Epoch 1/1... Discriminator Loss: 1.2803... Generator Loss: 0.7284
Epoch 1/1... Discriminator Loss: 1.4226... Generator Loss: 0.8388
Epoch 1/1... Discriminator Loss: 1.4157... Generator Loss: 0.8996
Epoch 1/1... Discriminator Loss: 1.3675... Generator Loss: 0.9570
Epoch 1/1... Discriminator Loss: 1.2886... Generator Loss: 1.0831
Epoch 1/1... Discriminator Loss: 1.3940... Generator Loss: 0.8672
Epoch 1/1... Discriminator Loss: 1.2976... Generator Loss: 0.9041
Epoch 1/1... Discriminator Loss: 1.3555... Generator Loss: 0.7478
Epoch 1/1... Discriminator Loss: 1.4996... Generator Loss: 0.8798
Epoch 1/1... Discriminator Loss: 1.3518... Generator Loss: 0.7719
Epoch 1/1... Discriminator Loss: 1.4177... Generator Loss: 0.7288
Epoch 1/1... Discriminator Loss: 1.3661... Generator Loss: 0.9231
Epoch 1/1... Discriminator Loss: 1.2889... Generator Loss: 0.9652
Epoch 1/1... Discriminator Loss: 1.3829... Generator Loss: 0.8605
Epoch 1/1... Discriminator Loss: 1.3312... Generator Loss: 0.6988
Epoch 1/1... Discriminator Loss: 1.5316... Generator Loss: 0.6844
Epoch 1/1... Discriminator Loss: 1.2056... Generator Loss: 0.8168
Epoch 1/1... Discriminator Loss: 1.3392... Generator Loss: 0.8414
Epoch 1/1... Discriminator Loss: 1.3492... Generator Loss: 0.8360
Epoch 1/1... Discriminator Loss: 1.3921... Generator Loss: 0.7595
Epoch 1/1... Discriminator Loss: 1.4519... Generator Loss: 0.7895
Epoch 1/1... Discriminator Loss: 1.2311... Generator Loss: 1.0281
Epoch 1/1... Discriminator Loss: 1.3552... Generator Loss: 1.0413
Epoch 1/1... Discriminator Loss: 1.2670... Generator Loss: 0.9688
Epoch 1/1... Discriminator Loss: 1.4655... Generator Loss: 0.6322
Epoch 1/1... Discriminator Loss: 1.2819... Generator Loss: 0.8069
Epoch 1/1... Discriminator Loss: 1.3996... Generator Loss: 0.8281
Epoch 1/1... Discriminator Loss: 1.3702... Generator Loss: 0.9143
Epoch 1/1... Discriminator Loss: 1.4119... Generator Loss: 0.8777
Epoch 1/1... Discriminator Loss: 1.3943... Generator Loss: 0.8781
Epoch 1/1... Discriminator Loss: 1.1625... Generator Loss: 1.0737
Epoch 1/1... Discriminator Loss: 1.4094... Generator Loss: 0.8917
Epoch 1/1... Discriminator Loss: 1.2666... Generator Loss: 0.9665
Epoch 1/1... Discriminator Loss: 1.3604... Generator Loss: 0.9441
Epoch 1/1... Discriminator Loss: 1.1904... Generator Loss: 0.8980
Epoch 1/1... Discriminator Loss: 1.3935... Generator Loss: 0.8945
Epoch 1/1... Discriminator Loss: 1.2963... Generator Loss: 1.0495
Epoch 1/1... Discriminator Loss: 1.3242... Generator Loss: 0.6799
Epoch 1/1... Discriminator Loss: 1.3468... Generator Loss: 1.0474
Epoch 1/1... Discriminator Loss: 1.1336... Generator Loss: 1.1240
Epoch 1/1... Discriminator Loss: 1.1948... Generator Loss: 0.8025
Epoch 1/1... Discriminator Loss: 1.1991... Generator Loss: 1.0973
Epoch 1/1... Discriminator Loss: 1.2651... Generator Loss: 0.8061
Epoch 1/1... Discriminator Loss: 1.2809... Generator Loss: 1.0681
Epoch 1/1... Discriminator Loss: 1.0256... Generator Loss: 1.2066
Epoch 1/1... Discriminator Loss: 1.1205... Generator Loss: 0.9922
Epoch 1/1... Discriminator Loss: 1.2574... Generator Loss: 1.0508
Epoch 1/1... Discriminator Loss: 1.3702... Generator Loss: 1.0671
Epoch 1/1... Discriminator Loss: 1.1600... Generator Loss: 1.0158
Epoch 1/1... Discriminator Loss: 1.3502... Generator Loss: 0.9539
Epoch 1/1... Discriminator Loss: 1.1733... Generator Loss: 0.7400
Epoch 1/1... Discriminator Loss: 1.1213... Generator Loss: 0.9466
Epoch 1/1... Discriminator Loss: 1.0902... Generator Loss: 1.2442
Epoch 1/1... Discriminator Loss: 1.2640... Generator Loss: 1.0633
Epoch 1/1... Discriminator Loss: 1.4418... Generator Loss: 0.9225
Epoch 1/1... Discriminator Loss: 1.3691... Generator Loss: 0.8869
Epoch 1/1... Discriminator Loss: 1.3012... Generator Loss: 0.7009
Epoch 1/1... Discriminator Loss: 1.2225... Generator Loss: 1.0437
Epoch 1/1... Discriminator Loss: 1.2036... Generator Loss: 1.2263
Epoch 1/1... Discriminator Loss: 1.4285... Generator Loss: 0.9575
Epoch 1/1... Discriminator Loss: 1.3116... Generator Loss: 0.8610
Epoch 1/1... Discriminator Loss: 1.4473... Generator Loss: 1.3558
Epoch 1/1... Discriminator Loss: 1.2804... Generator Loss: 0.9820
Epoch 1/1... Discriminator Loss: 1.3233... Generator Loss: 0.8453
Epoch 1/1... Discriminator Loss: 1.1337... Generator Loss: 0.9455
Epoch 1/1... Discriminator Loss: 1.2022... Generator Loss: 1.0429
Epoch 1/1... Discriminator Loss: 1.1865... Generator Loss: 1.1664
Epoch 1/1... Discriminator Loss: 1.1149... Generator Loss: 0.8832
Epoch 1/1... Discriminator Loss: 1.0232... Generator Loss: 1.0783

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.